Considering our novel context modelling of the personal context, the context learning, and the architecture presented in this paper, we can show as use cases the papers that individually compartmentalizes examples for these improvements.
Current works on context recognition focus on learning the relationship between the input data (sensor readings) and the target concepts (context). The structure of the context is implicitly learned by the implemented machine learning algorithm during the training phase. The new parts are described in Section  and compared with related work we can outline the following: 1) the connection between the learned context model; 2) The extension of new dimensions and classification of context, i.e. Internal State, Functions, Actions. The Algorithm  bridges the gap between the two modellings allowing context recognition to leverage both machine learning solutions and knowledge graph representation (ETG and EG). For instance, the ETG and EG can generate the questions needed by the machine to interact with the users.
For instance, the personal context recognition model shown in [1]}, showed to be a good approach to increase the accuracy of the context recognition algorithms. Our work described in Section  enhance the representation of the personal context with the hope to perform better in real-life scenarios.
In practice, existing approaches for context recognition using batch or streaming sensor data [2]}, [3]}, [4]} do not leverage on an explicit context modeling. The context representation is implicit in the labels used to train the machine learning model. The context formalization introduced in this work can be used to structure the output of these machine learning models according to our representation. Moreover, it can help machine learning approaches to interact with users. For instance, the machine can ask if Haonan is a friend of Xiaoyue since they are walking together. Approaches that use active learning strategies (e.g., [5]}, [6]}, [7]}) can benefit of our representation.
